Deep Learning for Continuous-time Stochastic Control with Jumps

• URL: http://arxiv.org/abs/2505.15602v1
• Date: Wed, 21 May 2025 14:57:39 GMT
• Title: Deep Learning for Continuous-time Stochastic Control with Jumps
• Authors: Patrick Cheridito, Jean-Loup Dupret, Donatien Hainaut,
• Abstract summary: We introduce a model-based deep-learning approach to solve finite-horizon continuous-time control problems with jumps.<n>We iteratively train two neural networks: one to represent the optimal policy and the other to approximate the value function.
• Score: 1.6112718683989882
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a model-based deep-learning approach to solve finite-horizon continuous-time stochastic control problems with jumps. We iteratively train two neural networks: one to represent the optimal policy and the other to approximate the value function. Leveraging a continuous-time version of the dynamic programming principle, we derive two different training objectives based on the Hamilton-Jacobi-Bellman equation, ensuring that the networks capture the underlying stochastic dynamics. Empirical evaluations on different problems illustrate the accuracy and scalability of our approach, demonstrating its effectiveness in solving complex, high-dimensional stochastic control tasks.

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